
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "8"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 110 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 105 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	39.610725	0.089319	66.418458	15.203533	20.710743	4.935686	37.113016	18.963916	0.0520	0.7620	0.5498	1.298349	14.413841
2459593	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	39.200818	2.666235	79.779672	17.746459	31.613316	4.463915	25.406179	4.912706	0.0534	0.7152	0.5223	1.275883	4.786458
2459592	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	3.236107	-0.142600	6.365571	3.026320	2.740959	0.413763	3.985188	0.019974	0.0305	0.0303	0.0016	nan	nan
2459591	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	34.276206	2.680672	94.379111	20.578676	27.298816	3.924831	14.360810	0.632566	0.0520	0.6647	0.4739	1.315229	4.458448
2459590	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	42.837846	3.081138	87.531133	19.143721	29.449848	2.816211	20.418128	1.209008	0.0565	0.6644	0.4720	1.364815	5.122294
2459589	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	37.174139	2.911480	81.539675	17.036062	17.160332	5.630025	23.756603	10.317020	0.0554	0.6819	0.5008	1.275393	4.260686
2459588	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	51.051606	1.473058	94.906870	20.254259	22.184300	9.766284	24.457037	10.119842	0.0582	0.7513	0.5737	1.301476	7.381974
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	355.217662	356.200979	inf	inf	41.031463	104.998735	256.472113	584.668448	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	35.288995	1.084185	31.192099	1.304054	10.108088	0.762223	24.980337	0.593529	0.0574	0.6696	0.4788	1.303013	3.855584
2459585	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.821165	4.628988	10.085558	3.422734	9.153677	2.443907	9.298695	0.985941	0.0336	0.0324	0.0017	0.000000	0.000000
2459584	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	28.539483	20.933994	109.887754	6.877842	14.988926	21.904124	-0.240608	-0.372596	0.0493	0.6258	0.4599	2.435947	6.752058
2459583	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	16.335850	1.911317	34.539847	5.863555	20.057899	0.297646	0.135348	0.600149	0.0555	0.6547	0.4912	1.175721	2.952224
2459582	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	14.363806	1.837378	31.076782	5.452638	24.260506	0.476444	0.211524	0.367188	0.0516	0.6617	0.4907	1.265564	3.655128
2459581	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	20.038720	2.879038	36.026137	5.653056	19.050854	1.093099	0.421507	0.802857	0.0565	0.6600	0.5000	1.229730	3.500999
2459580	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	15.274098	1.842897	36.167140	5.956534	21.227010	0.821356	-0.044938	0.345238	0.0582	0.6709	0.5000	1.266570	4.053651
2459579	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	15.001943	1.818281	40.960324	7.304221	23.622679	1.309710	1.317737	1.180900	0.0529	0.6688	0.4963	1.300327	4.378730
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.249096	-0.674128	2.988420	0.283430	0.853749	-0.400082	1.360251	0.008223	0.0320	0.0324	0.0020	nan	nan
2459577	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	19.650588	2.554101	33.452418	5.447868	21.145567	1.031515	0.275364	1.441709	0.0523	0.6727	0.5007	1.243665	3.848294
2459576	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	15.235327	2.032224	26.603285	4.588948	19.276441	2.514080	0.958042	1.464064	0.0546	0.6837	0.4511	0.000000	0.000000
2459575	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	15.527338	1.907397	26.319009	4.298915	15.810162	2.684336	7.234056	5.577169	0.0553	0.7365	0.4887	0.000000	0.000000
2459574	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	14.581538	1.950754	25.276812	4.210938	20.731773	2.362689	1.209036	0.949331	0.0515	0.6876	0.4266	1.175557	3.067526
2459573	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	15.237248	1.960351	43.493168	7.401122	24.924613	-0.105742	0.935961	1.307561	0.0521	0.6604	0.4468	0.000000	0.000000
2459572	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	14.918845	2.276595	25.404212	4.246582	16.149599	0.018827	2.178431	1.948822	0.0527	0.6653	0.4480	1.032250	2.975392
2459571	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.696781	-0.889692	2.899891	0.275194	1.408574	-0.399309	1.259985	0.010981	0.0349	0.0343	0.0021	nan	nan
2459570	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	19.591640	2.179355	33.376017	4.891886	10.458570	5.315211	8.276976	6.709605	0.0652	0.7350	0.4705	1.146025	3.481087
2459566	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	15.047576	3.083020	33.158671	5.537882	19.074002	0.947161	-0.272899	1.358299	0.0460	0.6641	0.4029	1.085614	1.171001
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	-2.569066	-2.569066	-2.410329	-2.410329	0.475242	0.475242	0.005642	0.005642	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	14.393319	0.355239	35.176516	-1.735105	2.399393	-2.164899	0.589565	0.273504	0.0739	0.6831	0.5087	1.192269	2.588184
2459563	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	16.464635	3.542928	67.118976	14.537650	19.688218	7.989742	2.758971	-0.680182	0.0684	0.6623	0.5044	0.000000	0.000000
2459562	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	10.781510	-0.259364	17.358835	-1.362550	2.586606	-2.741947	1.032550	-0.685673	0.0737	0.6764	0.5007	1.224871	2.856304
2459561	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	11.502064	0.433237	16.534391	-0.808116	2.857381	-2.418438	1.562924	-1.488094	0.0781	0.6788	0.5063	0.000000	0.000000
2459560	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	10.537169	0.106493	16.984166	-1.013783	2.694823	-1.741642	1.073938	-0.899587	0.0712	0.6716	0.4915	1.191189	3.114772
2459559	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	12.875515	0.478360	18.553840	-0.590694	3.054894	-1.500748	1.321843	-1.055236	0.0728	0.6823	0.5188	1.259002	2.582868
2459558	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	17.769624	3.808668	34.806290	7.438981	21.818333	7.201041	1.846636	-0.956972	0.0701	0.6746	0.5193	1.252977	2.698030
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0353	0.0318	0.0023	nan	nan
2459556	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	18.253402	4.069346	37.618447	7.638566	19.794468	6.391927	3.700387	-1.738699	0.0756	0.6827	0.5214	1.262006	2.785280
2459554	dish_ok	-	100.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0689	0.6715	0.5177	nan	nan
2459553	dish_ok	-	100.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0740	0.6784	0.5262	nan	nan
2459552	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	20.761523	6.497183	50.781812	11.127318	29.386715	11.984101	3.527105	0.227707	0.0624	0.6640	0.5169	0.000000	0.000000
2459551	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	10.334238	1.474874	20.561456	-1.052761	3.588064	-2.283818	1.402718	-0.867838	0.0687	0.6580	0.5027	1.247798	2.568263
2459550	dish_ok	-	98.35%	98.35%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0460	0.0457	-0.0002	nan	nan
2459549	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	15.243234	4.771043	38.200124	7.811065	21.170234	8.289427	1.658005	-0.414926	0.0626	0.6603	0.5081	1.202875	2.543542
2459542	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	22.231793	6.150513	77.093903	16.099555	1.152338	0.511782	1.175834	-0.755217	0.0738	0.7102	0.5295	1.256825	3.589960
2459541	dish_ok	100.00%	99.55%	1.79%	0.00%	100.00%	0.00%	11.331828	6.230799	38.278561	9.154315	223.662534	7.458939	8.067976	2.680355	0.0977	0.6917	0.5123	1.201104	3.120922
2459540	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	14.826550	6.741846	17.271610	0.300952	4.023757	-0.977869	0.842519	-0.736382	0.0771	0.6834	0.5044	1.227501	2.451697
2459536	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	27.467610	11.794911	66.075965	13.201017	29.286349	14.206029	3.199970	-0.717797	0.0665	0.6821	0.4467	1.224699	3.260425
2459535	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	11.378844	5.153288	16.056884	0.150234	2.109394	-0.759312	0.496727	-0.785605	0.0753	0.7073	0.4605	1.250913	2.472350
2459534	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	12.069657	5.465826	14.918877	-0.700776	2.348678	-1.489137	0.360096	-0.756984	0.0644	0.7080	0.3592	1.241115	2.696643
2459533	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	19.085924	10.167596	35.569680	7.976796	18.166743	8.386759	1.914692	-0.573007	0.0634	0.6626	0.3980	1.262083	2.608333
2459532	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	22.420452	11.607313	47.924194	10.184642	22.804366	11.122970	1.267857	-0.847826	0.0664	0.6585	0.4001	1.294155	2.858821
2459530	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	11.054065	4.969308	15.158113	-0.784496	3.614633	-1.463970	0.779105	-0.730929	0.0806	0.6805	0.3904	1.218002	2.539717
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.943356	2.752290	3.141360	3.044505	1.074977	1.583235	2.727996	0.688463	0.0353	0.0355	0.0024	nan	nan
2459508	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	28.208606	4.932054	50.206564	6.402014	18.255904	5.460779	12.063879	7.149656	0.1282	0.8941	0.6478	0.000000	0.000000
2459505	dish_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	24.749702	3.401947	47.237739	7.398041	7.707390	2.733505	2.240218	0.183853	0.0788	0.8318	0.5640	1.208692	5.766649

Antenna Season Report Notebook¶

Antenna 8 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 8, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 8: 2459634

Antenna 8: 2459632

Antenna 8: 2459631

Antenna 8: 2459630

Antenna 8: 2459629

Antenna 8: 2459628

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Antenna 8: 2459614

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Antenna 8: 2459609

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Antenna 8: 2459563

Antenna 8: 2459562

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Antenna 8: 2459559

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Antenna 8: 2459556

Antenna 8: 2459552

Antenna 8: 2459551

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶